vnpy backtesting for VSCode

backtesting.py

#%%
%load_ext autoreload
%autoreload 2

#%%
import matplotlib.pyplot as plt

plt.style.use("dark_background")

#%%
from vnpy.app.cta_strategy.backtesting import BacktestingEngine, OptimizationSetting

#%%Own Strategy
from super_multi_portfolio_strategy import SuperMultiPortfolioStrategy

from datetime import datetime

#%%
engine = BacktestingEngine()
engine.set_parameters(
    vt_symbol="RB88.SHFE",
    interval="1m",
    start=datetime(2019, 1, 1),
    end=datetime(2019, 7, 1),
    rate=3 / 10000,
    slippage=1,
    size=10,
    pricetick=1,
    capital=1_000_000,
)
#%%
engine.add_strategy(SuperMultiPortfolioStrategy, {})

# #%%
# engine.load_data()
# engine.run_backtesting()
# df = engine.calculate_result()
# engine.calculate_statistics()
# engine.show_chart()


setting = OptimizationSetting()
setting.set_target("sharpe_ratio")
setting.add_parameter("rsi_window", 5, 50, 2) # 14
setting.add_parameter("rsi_level", 5, 50, 2) # 20
setting.add_parameter("cci_window", 5, 50, 2) # 30
setting.add_parameter("cci_level", 5, 50, 2) # 10

engine.run_ga_optimization(setting)

portfolio.py

#%%
%load_ext autoreload
%autoreload 2


#%%
from datetime import datetime
from vnpy.app.cta_strategy.backtesting import BacktestingEngine, OptimizationSetting
from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import AtrRsiStrategy
from vnpy.app.cta_strategy.strategies.king_keltner_strategy import KingKeltnerStrategy
from vnpy.app.cta_strategy.strategies.dual_thrust_strategy import DualThrustStrategy
from vnpy.app.cta_strategy.strategies.multi_timeframe_strategy import (
    MultiTimeframeStrategy,
)

#%%
def run_backtesting(
    strategy_class,
    setting,
    vt_symbol,
    interval,
    start,
    end,
    rate,
    slippage,
    size,
    pricetick,
    capital,
):
    engine = BacktestingEngine()
    engine.set_parameters(
        vt_symbol=vt_symbol,
        interval=interval,
        start=start,
        end=end,
        rate=rate,
        slippage=slippage,
        size=size,
        pricetick=pricetick,
        capital=capital,
    )
    engine.add_strategy(strategy_class, setting)
    engine.load_data()
    engine.run_backtesting()
    df = engine.calculate_result()
    return df


def show_portafolio(df):
    engine = BacktestingEngine()
    engine.set_parameters(
        vt_symbol="IF88.CFFEX",
        interval="1m",
        start=datetime(2017, 1, 1),
        end=datetime(2019, 7, 1),
        rate=3 / 100000,
        slippage=0.2,
        size=300,
        pricetick=0.2,
        capital=4_000_000,
    )
    engine.calculate_statistics(df)
    engine.show_chart(df)


#%%
df1 = run_backtesting(
    strategy_class=AtrRsiStrategy,
    setting={},
    vt_symbol="IF88.CFFEX",
    interval="1m",
    start=datetime(2017, 1, 1),
    end=datetime(2019, 7, 1),
    rate=3 / 100000,
    slippage=0.2,
    size=300,
    pricetick=0.2,
    capital=1_000_000,
)


#%%
df2 = run_backtesting(
    strategy_class=KingKeltnerStrategy,
    setting={},
    vt_symbol="IF88.CFFEX",
    interval="1m",
    start=datetime(2017, 1, 1),
    end=datetime(2019, 7, 1),
    rate=3 / 100000,
    slippage=0.2,
    size=300,
    pricetick=0.2,
    capital=1_000_000,
)

# #%%
df3 = run_backtesting(
    strategy_class=MultiTimeframeStrategy,
    setting={},
    vt_symbol="IF88.CFFEX",
    interval="1m",
    start=datetime(2017, 1, 1),
    end=datetime(2019, 7, 1),
    rate=3 / 100000,
    slippage=0.2,
    size=300,
    pricetick=0.2,
    capital=1_000_000,
)

#%%
df4 = run_backtesting(
    strategy_class=DualThrustStrategy,
    setting={},
    vt_symbol="IF88.CFFEX",
    interval="1m",
    start=datetime(2017, 1, 1),
    end=datetime(2019, 7, 1),
    rate=3 / 100000,
    slippage=0.2,
    size=300,
    pricetick=0.2,
    capital=1_000_000,
)


#%%
dfp = df1 + df2 + df3 + df4
dfp = dfp.dropna()
show_portafolio(dfp)

#%%